We tend to accept the numbers displayed in GA as the absolute truth. It’s is a slippery slope. We should ask questions to figure out what’s behind the number. It’s not always easy to answer, but investing the time will be worth it.

With that in mind, let’s pull some data using the GA API to give it different shapes. We are trying to find a way to get to the answer faster and to go deeper into the visitor patterns. With a little bit of help from tools like R and Tableau our data analysis should go very smooth.

Let’s dive in!

Box plots & Histograms

In GA we don’t have any way to build box plot visualizations. It’s too bad because we can easily look at aggregations, spot outliers, see distributions, etc.

High-level executives usually love box plots. It might be an excellent way to communicate with them. But most importantly, Data Analysis should be a core discipline, not only a better way to get your point across.

The following is the most comprehensive video explanation I could find on box-and-whiskers charts (or box-plots)

We should keep in mind that taller means the data varies a lot, shorter means that it’s more stable and easier to predict.

Here is a chart we built using R showing traffic grouped by the day of the week:

We can quickly draw some conclusions from our chart:

Tuesday and Wednesday are the days with the highest traffic

Weekends are not very active, especially Saturdays

There are no outliers, which means no unusual traffic spikes

The days with high traffic have longer whiskers compared to lower traffic ones: it means that the variation is higher

Let’s have a look at another box plot, built with Tableau. It shows the daily traffic grouped by month of the year:

Insights:

it’s easy to spot the outliers in this example

it’s more difficult to predict traffic in November, December and January compared to the rest of the year (since the variations are taller)

these three are also the months with the highest traffic

Now, what if we could find a way to split the traffic by gender?

We have some data on the Demographics report. Why shouldn’t we try to create a cool viz?

Now, this is what I’m talking about! traffic for every day of the week split by gender.

Nicely done!

Let’s say that we want to compare this month’s conversion rate with the same period from last year.

Decomposing & forecasting time-series

Looking at time series is useful, but breaking them down into pieces is even more fruitful.

Decomposing all-time traffic in R

In the first chart we have our actual traffic which is broken down into:

trend: the big picture growth line

seasonal: our 7-day periods of time

random: the random noise in our traffic

Within a few strokes of the keyboard, we have a full picture of our traffic growth over time.

Forecasting

Below we have a traffic forecast for more than one year using the Holt-Winters algorithm in R:

With this chart, we try to answer the question of where will we be in one year if we continue doing the same stuff as before.

It’s not rocket science; it’s a mere projection that can help us see where we’re going.

We can get a similar result in Tableau. Here’s the same forecast as above, but looking at months instead of days:

What about the growth mindset? Let’s try it below!

Growth instead of traffic

Looking at growth instead of actual traffic brings a whole new mentality to the table.

We are forgetting about volumes and focusing only on improvement.

Here is a graph of the actual traffic for each month side by side compared with last year:

I would dare to say it’s an SEO’s dream chart. The traffic volume is looking good on a year over year basis!

If instead, you were to ask a growth hacker or a very ambitious business leader, they would probably be looking for other styles of comparisons.

For example, a percentage showing the year over year monthly growth:

Or something like a month over month growth for more extended periods of time:

If in the first chart everything was rainbows and sunshine, the following two show a slowdown in growth.

It could represent a problem in the big picture.

Cumulative growth

Seeing how the total sum of metrics on a graph can be insightful for specific campaigns.

The slope of growth is also a relevant indicator in my opinion.

Below is a chart with the cumulative number of sessions vs. transactions over one year:

We can even try to split it into multiple lines like traffic sources:

Compound growth

Growth in GA

I’ve managed to get to year over year growth reports in Google Analytics by asking some questions in the Intelligence box.

Not much wiggle room regarding time frames at the moment, but we can see the slowdown in growth with this graph as well.

Bubble charts

I love using bubble charts to look to look at the overall picture.

Let’s say that we want a quick overview of traffic sources. We could start digging into this kind of a table:

Or we can quickly create a bubble chart to see the whole picture in more a visual way.

Here we have transactions vs. sessions sized by conversion rate:

On the X-axis we have the transactions volume, on the Y-axis we have the traffic, and the size of each bubble is represented by the transactions/user metric (similar to conversion rate, but reported to users instead of sessions).

Not all people know, but Google Analytics has a similar chart type called motion chart.

Some might feel it’s even better since you can play it to show the movement for each day.

The main downside is that it’s built on Flash. There are compatibility issues, and some of them might even block the browser entirely.

My feeling is that motion charts could be discontinued shortly. They look cool though (if you manage to see some working ones).

You can obtain the motion functionality in Tableau as well. Here’s how it looks:

3 things we can’t do with Google Analytics

Add average or median lines through the graph:

Cluster the traffic sources and color them accordingly:

Play around with the bubble transparency to see the cramped ones:

Splendid!

80/20 analysis with Pareto charts

With Pareto charts we can quickly answer questions like:

Which are my top landing pages? The ones that bring in 80% of the total traffic?

Which are the landing pages that deliver 80% of the total conversions?

If you are using enhanced ecommerce, you could ask: What are the top 20% of products which bring in 80% of sales/profit?

Transactions per landing page on a Pareto chart:

We have to scroll down until we reach the 80% mark in transaction volume:

We have a very similar graph for organic traffic per landing page:

We colored them by conversion rate to make it even more insightful!

If you are looking for a way to build Pareto charts with Tableau, we recommend this quick guide.

Combining Bars with Lines

In my opinion, it’s one of the most effective types of reports. It’s simple, and it cuts through the clutter in a matter of seconds. It can give us the power to make lightning-fast decisions.

Traffic vs conversion rate per landing page

Three key paths to follow on this viz:

find out pages with high conversion rates and double down on them

figure out what these pages have compared to the ones with low conversion rates and implement changes on a macro level

direct more traffic to those pages to see what happens; either from external sources or from within the website

Smarter attribution using the Markov Model

My personal opinion is that the current Google Analytics attribution models are broken.

First touch, last touch, linear touch, etc. attributions are frankly not useful.

Google has a data-driven attribution product, which is available to premium users. There have been talks about making it available for free accounts, but you need a whole lot of data in your GA account.

The main author of the Canonicalized content. I am highly passionate about data analysis, visualization and whatever helps people reach informed answers faster. I love what I do, and I am working to improve speed in every aspect of my life. I find comfort in helping people so if you have a question give me a shout!

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